571 citations · 3.6k across the 80 of their papers we have counts for
82 papers · 1 filter
Delving into Motion-Aware Matching for Monocular 3D Object Tracking
Kuan-Chih Huang, Ming-Hsuan Yang, Yi-Hsuan Tsai
Recent advances of monocular 3D object detection facilitate the 3D multi-object tracking task based on low-cost camera sensors. In this paper, we find that the motion cue of object…
CiteTracker: Correlating Image and Text for Visual Tracking
Xin Li, Yuqing Huang, Zhenyu He +3
Existing visual tracking methods typically take an image patch as the reference of the target to perform tracking. However, a single image patch cannot provide a complete and preci…
Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking
Mingzhan Yang, Guangxin Han, Bin Yan +4
Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e…
Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
Cheng-Ju Ho, Chen-Hsuan Tai, Yi-Hsuan Tsai +2
Semi-supervised object detection is important for 3D scene understanding because obtaining large-scale 3D bounding box annotations on point clouds is time-consuming and labor-inten…
Beyond SOT: Tracking Multiple Generic Objects at Once
Christoph Mayer, Martin Danelljan, Ming-Hsuan Yang +3
Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in th…
Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble
Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li +3
Automatically estimating 3D skeleton, shape, camera viewpoints, and part articulation from sparse in-the-wild image ensembles is a severely under-constrained and challenging proble…
Physics-based Indirect Illumination for Inverse Rendering
Youming Deng, Xueting Li, Sifei Liu +1
We present a physics-based inverse rendering method that learns the illumination, geometry, and materials of a scene from posed multi-view RGB images. To model the illumination of…
Learning Visibility for Robust Dense Human Body Estimation
Chun-Han Yao, Jimei Yang, Duygu Ceylan +3
Estimating 3D human pose and shape from 2D images is a crucial yet challenging task. While prior methods with model-based representations can perform reasonably well on whole-body…
An Extendable, Efficient and Effective Transformer-based Object Detector
Hwanjun Song, Deqing Sun, Sanghyuk Chun +5
Transformers have been widely used in numerous vision problems especially for visual recognition and detection. Detection transformers are the first fully end-to-end learning syste…
Neural Rendering of Humans in Novel View and Pose from Monocular Video
Tiantian Wang, Nikolaos Sarafianos, Ming-Hsuan Yang +1
We introduce a new method that generates photo-realistic humans under novel views and poses given a monocular video as input. Despite the significant progress recently on this topi…
MC-Blur: A Comprehensive Benchmark for Image Deblurring
Kaihao Zhang, Tao Wang, Wenhan Luo +6
Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios. However, in most real-world images, bl…
Multi-Stage Progressive Image Restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan +4
Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synerg…
Low Light Image Enhancement via Global and Local Context Modeling
Aditya Arora, Muhammad Haris, Syed Waqas Zamir +4
Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNN…
GAN Inversion: A Survey
Weihao Xia, Yulun Zhang, Yujiu Yang +3
GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generato…
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention
Mohammad K. Ebrahimpour, Jiayun Li, Yen-Yun Yu +4
Deep Convolutional Neural Networks (CNNs) have been repeatedly proven to perform well on image classification tasks. Object detection methods, however, are still in need of signifi…
Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
Xueting Li, Sifei Liu, Kihwan Kim +3
Affordance modeling plays an important role in visual understanding. In this paper, we aim to predict affordances of 3D indoor scenes, specifically what human poses are afforded by…
Online Multi-Object Tracking with Dual Matching Attention Networks
Ji Zhu, Hua Yang, Nian Liu +3
In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework t…
Unseen Object Segmentation in Videos via Transferable Representations
Yi-Wen Chen, Yi-Hsuan Tsai, Chu-Ya Yang +2
In order to learn object segmentation models in videos, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data…
PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection
Nian Liu, Junwei Han, Ming-Hsuan Yang
In saliency detection, every pixel needs contextual information to make saliency prediction. Previous models usually incorporate contexts holistically. However, for each pixel, usu…
A Fusion Approach for Multi-Frame Optical Flow Estimation
Zhile Ren, Orazio Gallo, Deqing Sun +3
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has…
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
Yibing Song, Jiawei Zhang, Lijun Gong +5
We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled…
Deep Attentive Tracking via Reciprocative Learning
Shi Pu, Yibing Song, Chao Ma +2
Visual attention, derived from cognitive neuroscience, facilitates human perception on the most pertinent subset of the sensory data. Recently, significant efforts have been made t…
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang +2
Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven f…
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Yijun Li, Chen Fang, Jimei Yang +3
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame. In this work, we study the problem of generating co…